All talks are listed in Pacific Time (PDT).

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Opening Remarks
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Online Multiserver Convex Chasing and Optimization
Yuval Rabani (Hebrew University of Jerusalem)
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Multi-Task Optimal Experiment Design
Steffen Grunewalder (Lancaster University)
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Selfish Robustness and Equilibria in Multi-Player Bandits
Vianney Perchet (ENSAE & Criteo AI Lab)
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Discussion
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Break
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Pure Exploration Problems
Wouter Koolen (Centrum Wiskunde & Informatica)
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Gradient Descent-Ascent Provably Converges to Strict Local Minmax Equilibria with a Finite Timescale Separation
Lillian Ratliff (University of Washington)
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Learning Outcomes in Queueing Systems
Eva Tardos (Cornell)
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Discussion
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Pandora's Box with Correlations: Learning and Approximation
Shuchi Chawla (University of Wisconsin, Madison)
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Regret Minimization for Stochastic Shortest Paths
Yishay Mansour (Tel Aviv University)
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Robust Algorithms for Secretaries and Bandits
Anupam Gupta
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Discussion
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Break
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The Non-Stochastic Control Framework
Naman Agarwal (Google)
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Competitive Algorithms for Online Control
Yisong Yue (Caltech)
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Discussion
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A Unifying View of Optimism in Episodic Reinforcement Learning
Ciara Pike-Burke (Imperial College London)
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On the Complexity of Learning Good Policies With and Without Rewards
Emilie Kaufmann (CNRS & University of Lille)
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Model-Based Reinforcement Learning with Value-Targeted Regression
Mengdi Wang (Princeton University)
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Discussion
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Gather.town
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A Generalization Bound for Online Variational Inference
Pierre Alquier (Riken AIP)
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Beating the Curse of Dimensionality in High-Dimensional Optimal Stopping
David Goldberg (Cornell ORIE)
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Online Learning via Offline Greedy Algorithms: Applications in Market Design and Optimization
Negin Golrezaei (MIT)
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Discussion
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Break
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Country-Scale Bandit Implementation for Targeted COVID-19 Testing
Hamsa Bastani (Wharton School of the University of Pennsylvania)
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Multi-Player Multi-Armed Bandit: Can We Still Collaborate at Homes Without "Zoom"?
Yuanzhi Li (Carnegie Mellon University)
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Multiplayer Bandit Learning - From Competition to Cooperation
Simina Branzei (Purdue University)
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Discussion
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Representation Learning and Exploration in Reinforcement Learning
Akshay Krishnamurthy (Microsoft Research)
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Corruption Robust Exploration in Episodic Reinforcement Learning
Aleksandrs Slivkins (Microsoft Research NYC)
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On the Global Convergence and Approximation Benefits of Policy Gradient Methods
Daniel Russo (Columbia University)
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Discussion
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Break
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An Alternative Softmax Operator for Reinforcement Learning
Michael Littman (Brown University)
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What Are the Statistical Limits of Offline Reinforcement Learning With Function Approximation?
Sham Kakade (University of Washington & Microsoft Research)
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Discussion